Article Per Year (5 Year)
p-Index From 2020 - 2025
2.949
P-Index
This Author published in this journals
All Journal Jurnal Perpustakaan Pertanian Jurnal Infomedia Jurnal Vokasi SKANIKA: Sistem Komputer dan Teknik Informatika eProceeding of TIK Jurnal Teknologi Rekayasa Informasi dan Komputer
Hendrawaty Hendrawaty, Hendrawaty
Pusat Perpustakaan dan Penyebaran Teknologi Pertanian Jalan Ir. H. Juanda No. 20, Bogor 16122
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Library & Information Science Social Sciences

Published : 18 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : SKANIKA: Sistem Komputer dan Teknik Informatika

 1 
PENERAPAN DATA MINING UNTUK KLASTERISASI TINGKAT KEMISKINAN BERDASARKAN DATA TERPADU KESEJAHTERAAN SOSIAL (DTKS) Zuhendra, Muhammad Ihza; Hidayat, Rahmad; Hendrawaty, Hendrawaty
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 7 No 1 (2024): Jurnal SKANIKA Januari 2024
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v7i1.3149

Abstract

The level of poverty serves as a significant indicator influencing a nation's well-being. Poverty can arise from various factors, such as limited job opportunities resulting in insufficient income to cover living expenses, substantial family responsibilities, and more. In this context, the government plays a role by providing assistance, such as social aid programs. One step in providing this assistance involves individuals being registered as participants in the Unified Social Welfare Data (Data Terpadu Kesejahteraan Sosial or DTKS). Becoming a DTKS participant requires meeting the criteria categorizing someone as extremely poor, which is generally determined by the Minister of Social Affairs' Decision No. 146/HUK/2013 on the criteria for registered individuals in extreme poverty.This consideration can serve as a guideline in determining the socioeconomic status of community groups within a region. However, on a larger scale, classifying communities based on poverty levels can be a complex and time-consuming task. K-Means clustering is one of several non-hierarchical data clustering methods that work by partitioning existing data into one or more clusters or groups. This clustering can be applied to categorize a large dataset to enhance the accuracy of the information obtained, such as assessing the poverty level in a specific area. The objective of this research is to develop an application that facilitates the categorization of communities in analyzing the progression of poverty rates in a region based on predefined criteria. This aids the government and other stakeholders in understanding poverty distribution better, identifying high-risk groups, and designing targeted and effective social aid programs or policies. The outcomes of this research showcase visualizations depicting the percentage composition of each group within a dataset. The presented data visualizations can also be customized based on categories such as the number of clusters, regions, years, and more.
Co-Authors Adillah, adillah Ali, Sayed Khaidir Amri Amri Annisa, Rania Arhami, Muhammad Armaya, Nursella Indah Atthariq, Atthariq Azhar Azhar Etty Andriaty Fariz Maulana, Muhammad Fata, Akmalul Fazil, Fazil Huzaeni, Huzaeni Kurniawan, Ahmad Mahdi Mahdi, Mahdi Maulida, Khaira Muhammad Rizka, Muhammad Mulyadi Mulyadi Munanzar, Arief Nadia, Khaira Reza Fauzan Salahuddin Salahuddin Salwa, Safira Sariadi Sariadi Sugeng, Santoso Tumangger, Mhd Taufik Alhamdi Pratama Zuhendra, Muhammad Ihza Zuhratul Aini, Zuhratul